Utilizing patient geographic information system data to plan telemedicine service locations

Abstract

Objective: To understand potential utilization of clinical services at a rural integrated health care system by generating optimal groups of telemedicine locations from electronic health record (EHR) data using geographic information systems (GISs).

Methods: This retrospective study extracted nonidentifiable grouped data of patients over a 2-year period from the EHR, including geomasked locations. Spatially optimal groupings were created using available telemedicine sites by calculating patients' average travel distance (ATD) to the closest clinic site.

Results: A total of 4027 visits by 2049 unique patients were analyzed. The best travel distances for site groupings of 3, 4, 5, or 6 site locations were ranked based on increasing ATD. Each one-site increase in the number of available telemedicine sites decreased minimum ATD by about 8%. For a given group size, the best groupings were very similar in minimum travel distance. There were significant differences in predicted patient load imbalance between otherwise similar groupings. A majority of the best site groupings used the same small number of sites, and urban sites were heavily used.

Discussion: With EHR geospatial data at an individual patient level, we can model potential telemedicine sites for specialty access in a rural geographic area. Relatively few sites could serve most of the population. Direct access to patient GIS data from an EHR provides direct knowledge of the client base compared to methods that allocate aggregated data.

Conclusion: Geospatial data and methods can assist health care location planning, generating data about load, load balance, and spatial accessibility.

Keywords: electronic health records; geographic information systems; service planning; telemedicine.

Figure 1.

Best average patient travel distances for groupings of 3, 4, 5, or 6 site locations. Site groupings are ranked on the y-axis by increasing ATD.

Figure 2.

Highest and lowest traffic for the extreme sites of each grouping, plotted against average patient travel distance for the groupings.

Figure 3.

Frequency of occurrence of each potential site in the top 30 groupings ranked by lowest average patient travel distance, for groupings of 4 sites. The principal location was forced into each grouping. Patient locations aggregated to county level.